Langgraph Agents

<objective> Build production-grade multi-agent systems with LangGraph using supervisor, swarm, or master patterns. Enables cost-optimized orchestration with multi-provider routing (Claude, DeepSeek, Gemini - NO OpenAI), proper state management, and scalable agent coordination. </​objective>

<quick_start> State schema (foundation):

from typing import TypedDict, Annotated
from langgraph.graph import add_messages

class AgentState(TypedDict, total=False):
    messages: Annotated[list, add_messages]  # Auto-merge
    next_agent: str  # For handoffs

Pattern selection:

Multi-provider: Use lang-core for auto-selection by cost/​quality/​speed </​quick_start>

<success_criteria> Multi-agent system is successful when:

• State uses Annotated[..., add_messages] for proper message merging
• Termination conditions prevent infinite loops
• Routing uses conditional edges (not hardcoded paths)
• Cost optimization: simple tasks → cheaper models (DeepSeek)
• Complex reasoning → quality models (Claude)
• NO OpenAI used anywhere
• Checkpointers enabled for context preservation </​success_criteria>

<core_content> Production-tested patterns for building scalable, cost-optimized multi-agent systems with LangGraph and LangChain.

When to Use This Skill

Symptoms:

• "State not updating correctly between agents"
• "Agents not coordinating properly"
• "LLM costs spiraling out of control"
• "Need to choose between supervisor vs swarm patterns"
• "Unclear how to structure agent state schemas"
• "Agents losing context or repeating work"

Use Cases:

• Multi-agent systems with 3+ specialized agents
• Complex workflows requiring orchestration
• Cost-sensitive production deployments
• Self-learning or adaptive agent systems
• Enterprise applications with multiple LLM providers

Quick Reference: Orchestration Pattern Selection

Core Patterns

1. State Schema (Foundation)

from typing import TypedDict, Annotated, Dict, Any
from langchain_core.messages import BaseMessage
from langgraph.graph import add_messages

class AgentState(TypedDict, total=False):
    messages: Annotated[list[BaseMessage], add_messages]  # Auto-merge
    agent_type: str
    metadata: Dict[str, Any]
    next_agent: str  # For handoffs

Deep dive: reference/​state-schemas.md (reducers, annotations, multi-level state)

2. Multi-Provider Configuration (via lang-core)

# Use lang-core for unified provider access (NO OPENAI)
from lang_core.providers import get_llm_for_task, LLMPriority

# Auto-select by priority
llm_cheap = get_llm_for_task(priority=LLMPriority.COST)   # DeepSeek
llm_smart = get_llm_for_task(priority=LLMPriority.QUALITY)  # Claude
llm_fast = get_llm_for_task(priority=LLMPriority.SPEED)   # Cerebras
llm_local = get_llm_for_task(priority=LLMPriority.LOCAL)  # Ollama

Deep dive: reference/​base-agent-architecture.md, reference/​cost-optimization.md Infrastructure: See lang-core package for middleware, tracing, caching

3. Tool Organization

# Modular, testable tools
def create_agent_with_tools(llm, tools: list):
    return create_react_agent(llm, tools, state_modifier=state_modifier)

# Group by domain
research_tools = [tavily_search, wikipedia]
data_tools = [sql_query, csv_reader]

Deep dive: reference/​tools-organization.md

4. Supervisor Pattern (Centralized)

members = ["researcher", "writer", "reviewer"]
system_prompt = f"Route to: {members}. Return 'FINISH' when done."
supervisor_chain = prompt | llm.bind_functions([route_function])

5. Swarm Pattern (Distributed)

# Agents hand off directly
def agent_node(state):
    result = agent.invoke(state)
    return {"messages": [result], "next_agent": determine_next(result)}

workflow.add_conditional_edges("agent_a", route_to_next, {
    "agent_b": "agent_b", "agent_c": "agent_c", "end": END
})

Reference Files (Deep Dives)

• reference/​state-schemas.md - TypedDict, Annotated reducers, multi-level state
• reference/​base-agent-architecture.md - Multi-provider setup, agent templates
• reference/​tools-organization.md - Modular tool design, testing patterns
• reference/​orchestration-patterns.md - Supervisor vs swarm vs master (decision matrix)
• reference/​context-engineering.md - Memory compaction, just-in-time loading
• reference/​cost-optimization.md - Provider routing, caching, token budgets

Common Pitfalls

lang-core Integration

For production deployments, use lang-core for:

• Middleware: Cost tracking, budget enforcement, retry, caching, PII safety
• LangSmith: Unified tracing with @traced_agent decorators
• Providers: Auto-selection via get_llm_for_task(priority=...)
• Celery: Background agent execution with progress tracking
• Redis: Distributed locks, rate limiting, event pub/​sub

# Example: Agent with full lang-core stack
from lang_core import traced_agent, get_llm_for_task, LLMPriority
from lang_core.middleware import budget_enforcement_middleware, cost_tracking_middleware

@traced_agent("QualificationAgent", tags=["sales"])
async def run_qualification(data):
    llm = get_llm_for_task(priority=LLMPriority.SPEED)
    # ... agent logic